CN114693684A - Airborne fan blade defect detection method - Google Patents
Airborne fan blade defect detection method Download PDFInfo
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- CN114693684A CN114693684A CN202210613508.3A CN202210613508A CN114693684A CN 114693684 A CN114693684 A CN 114693684A CN 202210613508 A CN202210613508 A CN 202210613508A CN 114693684 A CN114693684 A CN 114693684A
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Abstract
The invention discloses a defect detection method for an airborne fan blade, which comprises the following steps of firstly obtaining a surface image of the fan blade, then converting the surface image into a gray image, then continuously traversing all pixels of a picture to generate a Tenengrad gradient matrix, judging a fan blade area based on a Tenengrad gradient threshold value, then traversing all pixels of the current picture to generate a Hoyer statistical value matrix, further reserving Hoyer statistical values of the fan blade area in the step according to the judgment condition of the fan blade area, setting the Hoyer statistical values of other areas to zero, finally setting a Hoyer statistical value threshold value, and if the Hoyer statistical value is larger than the threshold value, determining that the pixel point is a defect characteristic. The method directly identifies the defects of the fan blade from the Tenengrad gradient and the Hoyer statistical value, and has the characteristic of being not influenced by the distribution of training data samples.
Description
Technical Field
The invention relates to the technical field of surface quality detection of wind power facilities, in particular to a method for detecting defects of blades of an airborne fan.
Background
The fan blade is used as a wind catching mechanism of the wind driven generator and can be influenced by the fan blade or the outside in the operation process. If the wind power blade is damaged, the wind power blade is light and becomes economic loss, and is heavy and generates potential safety hazard. In the conventional monitoring method of the fan blade at present during regular inspection, maintenance personnel use a telescope to observe the state of the blade at a long distance and record the state on a log. However, the manual detection method inevitably has the problems of long detection time, low precision, high cost and the like, and is difficult to detect the large-scale fan blades of the wind field.
Disclosure of Invention
The invention aims to provide a method for detecting defects of an airborne fan blade. The invention can realize reliable and efficient state monitoring and defect identification of the fan blade and has the characteristic of no influence of training data sample distribution.
The technical scheme of the invention is as follows: a method for detecting defects of an airborne fan blade comprises the following steps:
s1: acquiring a surface image of a fan blade, and converting the surface image of the fan blade into a gray image;
s2: setting a sliding window with the window size of w1 multiplied by w1, traversing all pixels of the current gray picture by using the sliding window, and calculating the Tenengrad gradient of the pixels in the current window to form a Tenengrad gradient matrix;
s3: setting a Tenengrad gradient threshold, and if the Tenengrad gradient is greater than the threshold, setting the pixel point as a fan blade area;
s4: setting a sliding window with the window size of w2 multiplied by w2, traversing all pixels of the current gray picture by using the sliding window, and calculating the Hoyer statistical value of the pixels in the current window until all pixels of the current picture are traversed to form a Hoyer statistical value matrix;
s5: keeping the Hoyer statistical value of the fan blade area in the step S4 and setting the Hoyer statistical values of other areas to zero;
s6: and setting a Hoyer statistical value threshold, and if the Hoyer statistical value of the fan blade area is greater than the threshold, determining that the pixel point is a defect characteristic.
In the method for detecting the defects of the blades of the airborne fan, the algorithm of the Tenengrad gradient is as follows:
whereinAs the Tenengrad gradient information for the current sliding window,,for the row and column index numbers of the sliding window of the image respectively,is the size of the window or windows,is a pointThe information of the gradient in the x-direction,is a pointGradient information along the y-direction.
In the method for detecting the defects of the blades of the airborne fan, the judgment algorithm of the fan blade area is as follows:
whereinThe judgment result of the current pixel point is that 1 represents that the current pixel point is positioned on the fan blade, 0 represents that the current pixel point is positioned outside the fan blade area,,for the row and column index numbers of the sliding window of the image respectively,the Tenengrad gradient corresponding to the current pixel point,is a preset threshold of Tenengrad gradient.
In the method for detecting the defects of the blades of the airborne fan, the algorithm of the Hoyer statistical value is as follows:
whereinFor the Hoyer statistic for the current sliding window,,for the row and column index numbers of the sliding window of the image respectively,is the size of the window(s),is a pointThe gray scale information of (2).
In the method for detecting the defects of the blades of the airborne fan, in step S5, the algorithm for retaining the Hoyer statistical value is as follows:
whereinFor the kept Hoyer statistics,is a matrix of the Hoyer statistical values,is the judgment result of the pixel points in the fan blade area,,are the row-column index numbers of the image sliding window respectively.
In the method for detecting the defect of the onboard fan blade, the algorithm for judging the defect characteristics of the fan blade is as follows:
wherein the content of the first and second substances,is the defect judgment result of the current pixel point, 1 represents that the current pixel point is the defect position, 0 represents that the current pixel point does not contain defect information,,for the row and column index numbers of the sliding window of the image respectively,the Hoyer statistic value after being reserved for the current pixel point,is a preset Hoyer statistical value threshold value.
According to the method for detecting the defects of the blades of the airborne fan, the sliding window is moved to the upper left corner of the gray image, and one pixel is sequentially moved from left to right and from top to bottom until the sliding window traverses all pixels of the current gray image.
Compared with the prior art, the invention has the following beneficial effects:
the method comprises the steps of firstly obtaining a fan blade surface image, secondly converting the fan blade surface image into a gray level image, then traversing all pixels of a picture by using a sliding window to generate a Tenengrad gradient matrix, judging a fan blade area based on a Tenengrad gradient threshold, then continuously traversing all pixels of the current picture by using the sliding window to generate a Hoyer statistical value matrix, further reserving Hoyer statistical values of the fan blade area in the step according to the judgment condition of the fan blade area, setting the Hoyer statistical values of other areas to zero, finally setting a Hoyer statistical value threshold, and if the Hoyer statistical value is larger than the threshold, determining that a pixel point is a defect characteristic. The method directly identifies the defects of the fan blade from the Tenengrad gradient and the Hoyer statistical value, and has the characteristic of being not influenced by the distribution of training data samples. The invention can realize reliable and efficient state monitoring and defect identification of the fan blade.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a fan blade surface image used in example 1 of the present invention;
FIG. 3 is a Tenengrad gradient matrix formed in example 1 of the present invention;
fig. 4 is a fan blade area judgment result obtained in embodiment 1 of the present invention;
FIG. 5 is a Hoyer statistic matrix formed in example 1 of the present invention;
FIG. 6 shows the results of judging the defective characteristics in example 1 of the present invention;
FIG. 7 is a fan blade surface image used in example 2 of the present invention;
FIG. 8 is a Tenengrad gradient matrix formed in example 2 of the present invention;
fig. 9 is a fan blade area judgment result obtained in embodiment 2 of the present invention;
FIG. 10 is a Hoyer statistical value matrix formed in example 2 of the present invention;
fig. 11 shows the judgment result of the defect characteristics in example 2 of the present invention.
Detailed Description
The invention is further illustrated by the following figures and examples, which are not to be construed as limiting the invention.
Example 1: the invention relates to a method for detecting defects of an airborne fan blade, which is further described by combining specific cases, and a flow chart of the method is shown as a figure 1, and comprises the following steps,
1) fixing a shooting device on the unmanned aerial vehicle, and shooting a fan blade to obtain a surface image of the fan blade, as shown in the attached figure 2;
2) converting the blade surface image to a grayscale image;
3) setting the sliding window width to be w1, forming the sliding window with the window size of w1 xw 1, wherein the window width is 21 in the example;
4) moving the sliding window to the upper left corner of the gray image, and calculating the Tenengrad gradient of the elements in the current window (the Tenengrad gradient function adopts a Sobel operator to respectively extract gradient values in the horizontal direction and the vertical direction), wherein the algorithm of the Tenengrad gradient is as follows:
whereinAs the Tenengrad gradient information for the current sliding window,,for the row and column index numbers of the sliding window of the image respectively,is the size of the window or windows,is a pointThe information of the gradient in the x-direction,is a pointGradient information along the y-direction.
5) Moving the sliding window to the right by one pixel, and calculating the Tenengrad gradient of the element in the current window;
6) continuously traversing all pixels of the current picture to form a Tenengrad gradient matrix, as shown in figure 3;
7) setting a Tenengrad gradient threshold, if the Tenengrad gradient is greater than the threshold, taking the pixel as a fan blade area, and adopting a fan blade area judgment algorithm as follows:
whereinThe judgment result of the current pixel point is that 1 represents that the current pixel point is positioned on the fan blade, 0 represents that the current pixel point is positioned outside the fan blade area,,for the row and column index numbers of the sliding window of the image respectively,the Tenengrad gradient corresponding to the current pixel point,is a preset threshold of Tenengrad gradient. In this example, the threshold is 0.01, and the determination result is shown in fig. 4;
8) setting the width of the sliding window to be w2, forming a sliding serial port with the window size of w2 xw 2, wherein the window width is 3 in the example;
9) as a typical sparsity evaluation index, the Hoyer statistical value is very sensitive to the sample distribution condition, and the Hoyer statistical value is adopted to evaluate the distortion degree of the sliding window. Moving the sliding window to the upper left corner of the gray image, and calculating the Hoyer statistical value (the distortion measurement value, reflecting the size of sparsity) of the elements in the current window, wherein the Hoyer statistical value algorithm is as follows:
whereinFor the Hoyer statistic for the current sliding window,,for the row and column index numbers of the sliding window of the image respectively,is the size of the window or windows,is a pointThe gray scale information of (1).
10) Moving the sliding window to the right by one pixel, and calculating the Hoyer statistical value of the element in the current window;
11) continuously traversing all pixels of the current picture to form a Hoyer statistical value matrix, as shown in figure 5;
12) reserving the Hoyer statistical value of the fan blade area and setting the Hoyer statistical value of other areas to zero, wherein the specific algorithm for reserving the Hoyer statistical value is as follows:
whereinFor the kept Hoyer statistics,is a matrix of the Hoyer statistical values,is the judgment result of the pixel points in the fan blade area,,are the row-column index numbers of the image sliding window respectively.
13) Setting a Hoyer statistical value threshold, if the Hoyer statistical value is greater than the threshold, the pixel point is a defect characteristic, and the algorithm for judging the defect characteristic of the fan blade is as follows:
wherein the content of the first and second substances,the defect judgment result of the current pixel point is that 1 indicates that the current pixel point is a defect position, 0 indicates that the current pixel point does not contain defect information,,for the row and column index numbers of the sliding window of the image respectively,the Hoyer statistic value after being reserved for the current pixel point,is a preset Hoyer statistical value threshold value. In this example, the threshold is 0.01, and the specific result is shown in FIG. 6, which is clear from FIG. 6The fan blade defect characteristics are apparent.
Example 2: the invention relates to a method for detecting defects of an airborne fan blade, which is further explained by combining specific cases, and a flow chart of the method is shown as a figure 1, and comprises the following steps,
1) fixing a shooting device on the unmanned aerial vehicle, and shooting the fan blade to obtain a surface image of the fan blade, as shown in the attached figure 7;
2) converting the blade surface image to a grayscale image;
3) setting the sliding window width to w1, the window size is w1 × w1, in this example, the window width is 21;
4) moving the sliding window to the upper left corner of the gray image, and calculating the Tenengrad gradient of the elements in the current window, wherein the algorithm of the Tenengrad gradient is as follows:
whereinAs the Tenengrad gradient information for the current sliding window,,for the row and column index numbers of the sliding window of the image respectively,is the size of the window or windows,is a pointThe information of the gradient in the x-direction,is a pointGradient information along the y-direction.
5) Moving the sliding window to the right by one pixel, and calculating the Tenengrad gradient of the element in the current window;
6) continuously traversing all pixels of the current picture to form a Tenengrad gradient matrix, as shown in figure 8;
7) setting a Tenengrad gradient threshold, if the Tenengrad gradient is greater than the threshold, taking the pixel as a fan blade area, and adopting a fan blade area judgment algorithm as follows:
whereinThe judgment result of the current pixel point is that 1 represents that the current pixel point is positioned on the fan blade, 0 represents that the current pixel point is positioned outside the fan blade area,,for the row and column index numbers of the sliding window of the image respectively,the Tenengrad gradient corresponding to the current pixel point,is a preset threshold of Tenengrad gradient. In this example, the threshold is 0.01, and the determination result is shown in FIG. 9;
8) setting the sliding window width to w2, the window size is w2 × w2, in this example, the window width is 3;
9) as a typical sparsity evaluation index, the Hoyer statistical value is very sensitive to the sample distribution condition, and the Hoyer statistical value is adopted to evaluate the distortion degree of the sliding window. Moving the sliding window to the upper left corner of the gray image, and calculating the Hoyer statistical value of the element in the current window, wherein the Hoyer statistical value algorithm is as follows:
whereinFor the Hoyer statistic for the current sliding window,,for the row and column index numbers of the sliding window of the image respectively,is the size of the window or windows,is a pointThe gray scale information of (1).
10) Moving the sliding window to the right by one pixel, and calculating the Hoyer statistical value of the element in the current window;
11) continuously traversing all pixels of the current picture to form a Hoyer statistical value matrix, as shown in figure 10;
12) reserving the Hoyer statistical value of the fan blade area and setting the Hoyer statistical value of other areas to zero, wherein the specific algorithm for reserving the Hoyer statistical value is as follows:
whereinFor the kept Hoyer statistics,is a matrix of the Hoyer statistical values,is the judgment result of the pixel points in the fan blade area,,are the row-column index numbers of the image sliding window respectively.
13) Setting a Hoyer statistical value threshold, if the Hoyer statistical value is greater than the threshold, the pixel point is a defect characteristic, and the algorithm for judging the defect characteristic of the fan blade is as follows:
wherein the content of the first and second substances,is the defect judgment result of the current pixel point, 1 represents that the current pixel point is the defect position, 0 represents that the current pixel point does not contain defect information,,for the row and column index numbers of the sliding window of the image respectively,the Hoyer statistic value after being reserved for the current pixel point,is a preset Hoyer statistical value threshold value. The threshold value is 0.003 in the present example, and the specific result is shown in fig. 11, and the defect characteristics of the fan blade can be obviously seen from fig. 11.
In conclusion, the method directly identifies the defects of the fan blade from the Tenengrad gradient and the Hoyer statistical value, has the characteristic of no influence of training data sample distribution, and can realize reliable and efficient state monitoring and defect identification of the fan blade.
Claims (7)
1. A method for detecting defects of blades of an airborne fan is characterized by comprising the following steps: the method comprises the following steps:
s1: acquiring a surface image of a fan blade, and converting the surface image of the fan blade into a gray image;
s2: setting a sliding window with the window size of w1 multiplied by w1, traversing all pixels of the current gray picture by using the sliding window, and calculating the Tenengrad gradient of the pixels in the current window to form a Tenengrad gradient matrix;
s3: setting a Tenengrad gradient threshold, and if the Tenengrad gradient is greater than the threshold, setting the pixel point as a fan blade area;
s4: setting a sliding window with the window size of w2 multiplied by w2, traversing all pixels of the current gray picture by using the sliding window, and calculating the Hoyer statistical value of the pixels in the current window until all pixels of the current picture are traversed to form a Hoyer statistical value matrix;
s5: keeping the Hoyer statistical value of the fan blade area in the step S4 and setting the Hoyer statistical values of other areas to zero;
s6: and setting a Hoyer statistical value threshold, and if the Hoyer statistical value of the fan blade area is greater than the threshold, determining that the pixel point is a defect characteristic.
2. The method for detecting the defects of the blades of the airborne fan according to claim 1, wherein the method comprises the following steps: the algorithm of the Tenengrad gradient is as follows:
whereinAs the Tenengrad gradient information for the current sliding window,,for the row and column index numbers of the sliding window of the image respectively,is the size of the window or windows,is a pointThe information of the gradient in the x-direction,is a pointGradient information along the y-direction.
3. The method for detecting the defects of the blades of the airborne fan according to claim 1, wherein the method comprises the following steps: the judgment algorithm of the fan blade area is as follows:
whereinThe judgment result of the current pixel point is 1The front pixel point is positioned on the fan blade, 0 represents that the current pixel point is positioned outside the fan blade area,,for the row and column index numbers of the sliding window of the image respectively,the Tenengrad gradient corresponding to the current pixel point,is a preset threshold of Tenengrad gradient.
4. The method for detecting the defects of the blades of the airborne fan as claimed in claim 1, wherein the Hoyer statistic value algorithm is as follows:
5. The method for detecting the defects of the blades of the airborne fan as claimed in claim 1, wherein in the step S5, the algorithm for retaining the Hoyer statistic is as follows:
6. The method for detecting the defects of the blades of the airborne fan according to claim 1, wherein the algorithm for judging the characteristics of the defects of the blades of the fan is as follows:
wherein the content of the first and second substances,is the defect judgment result of the current pixel point, 1 represents that the current pixel point is the defect position, 0 represents that the current pixel point does not contain defect information,,for the row and column index numbers of the sliding window of the image respectively,the Hoyer statistic value after being reserved for the current pixel point,is a preset Hoyer statistical value threshold value.
7. The method for detecting the defects of the blades of the airborne fan according to claim 1, wherein the method comprises the following steps: and moving the sliding window to the upper left corner of the gray image, and sequentially moving one pixel from left to right and from top to bottom until the sliding window traverses all pixels of the current gray image.
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